1882results about How to "Efficient acquisition" patented technology

The invention discloses a method and device for obtaining a lane line. In one specific implementation mode, the method includes the steps that road data are collected, wherein the road data at least comprise the two-dimensional image data and the three-dimensional laser point cloud data; a machine learning method is adopted for recognizing the lane line in the two-dimensional image data; based on the spatial position relation between the two-dimensional image data and the three-dimensional laser point cloud data, a lane line recognition result is converted into three-dimensional lane line disperse points; according to statistical characteristics, the three-dimensional lane line disperse points are filtered; the filtered lane line disperse points are clustered so as to obtain the lane line. By means of the implementation mode, the lane line can be accurately and efficiently obtained, and it is avoided that vehicles or road edges or other marking lines in a road image influence the process of obtaining the lane line. When applied to map drawing, the drawing speed can be obviously increased, and the drawing precision can be obviously improved.

The invention discloses a method used in uplink data transmission, a terminal device and a network device. The method comprises the following steps: a network device generates configuration information for a CTU (Contention Transmission Unit) of an uplink first sub-band, wherein the first sub-band is one sub-band of multiple uplink sub-bands, the multiple uplink sub-bands are respectively provided with a special configuration, and the CTU is a resource unit used for carrying out grant free transmission on the first sub-band; and the network device sends the configuration information of the CTU. According to the method used in the uplink data transmission, the terminal device and the network device provided by the embodiment of the invention, the terminal device can obtain the information of the CTU quickly and effectively, thereby carrying out the grant free transmission; and the efficiency of a system to transmit data can be improved.

The invention discloses a method and a system for improving the large data volume query performance and belongs to the technical field of large data volume query. The method comprises A, loading data in a disk database into distributed caches in a cache ID-entity data key value pair mode, and storing the cache ID and key information of the entity data in a cache ID table of a memory database simultaneously; B, querying the cache ID table according to a query request when the query request sent by a client is obtained to selecting an ID set meeting the query request; C, obtaining the entity data from corresponding distributed caches according to the cache ID set and returning the entity data to the client. By means o the system and the method, loads of the disk database can be effectively reduced, and the big data query performance is improved.

Methods and apparatuses for acquiring/providing available channel information using Generic Advertisement Service (GAS) protocol in a wireless local area network (WLAN) are disclosed. AP (Access Point) station has a capability to acquire information for available channels from a database using a specific protocol related to an operation within the White Space (e.g. Access Network Query Protocol (ANQP)). And, non-AP station can retrieve the information for available channels by transmitting a Generic Advertisement Service (GAS) initial request frame to the AP station, wherein the GAS initial request frame comprises an Advertisement Protocol Element whose element ID indicates the specific protocol, and receiving a GAS initial response frame comprising a White Space Map (WSM) the specific protocol. The WSM comprises one or more white space channel units, and each of the white space channel units comprises a channel number field indicating one of the available channels and a power constraint field indicating a maximum allowable power on the indicated channel.

The invention belongs to the application technical field of AR intelligent glasses, and discloses a local image recognition method and a system based on the AR intelligent glasses. The method comprises steps calibrating the consistency of an intelligent glasses imaging screen, a front camera picture of the intelligent glasses and a visual field picture of a real environment around the intelligentglasses; recognizing the human eye image and calculating the eye motion vector to obtain the eye motion coordinates; identifying whether the focal point of the extended line of human binocular visionis an imaging screen or a real three-dimensional space; obtaining the mapping relationship of human eyes in the real world around them; according to the embedded mapping algorithm, the coordinates ofthe human eye fixation point on the glasses imaging screen and the front camera image being obtained respectively. The invention obtains the eye image information of the human eye through the eye movement instrument, synchronously calibrates the eye image and the scene camera, obtains the fixation point area of the human eye in the real scene, and performs image recognition only on the fixation point area during processing, thereby greatly reducing the image processing pressure of the GPU and improving the image processing efficiency.

The present invention relates to a method for improving the efficiency of dimensional acquisition of an object by a dimensional measurement device directed over the object, comprising the steps: a) directing the measurement device over the object to acquire its dimensions, b) providing an indication of the resolution of the acquired regions, c) re-directing the measurement device over at least part of the acquired regions indicating insufficient resolution according to predetermined criteria, d) updating the indication of the resolution of the acquired regions, and e) repeating steps c) and d) until sufficient resolution is indicated according to the predetermined criteria, thereby efficiently acquiring the dimensions of the object at sufficient resolution. It also relates to a computer program therefor.

The invention provides a refinement production system and a production method based on RFID and bar code fusion, wherein an RFID electronic tag is bound on the goods in process, the RFID electronic tag or a bar code can be selected randomly to be bound on the components and parts, thus being capable of effectively controlling production cost, being beneficial to carry out real time and effective collection on the information of goods in process and components and parts simultaneously, realizing accurate synchronization of material flow and information flow, and providing a possibility for therealization of refinement production for enterprises. The production method in the invention comprises the following steps: 1) binding the RFID electronic tags on the goods in process which enter a production line, and binding the bar codes or the RFID electronic tags on the components and parts; 2) when the components and parts which are pasted with bar codes or bound with the RFID electronic tags are on the production line, collecting the information of the components and parts to match with the information of the goods in process, and binding the information of components and parts with theinformation of the goods in process after matching successively; and 3) when the products are finally off the line, collecting the RFID electronic tag information which is bound on the goods in process and printing manufacturer certificates and bar codes.

The present invention discloses a method for intelligent automatic identification of transmission circuit parts. The method comprises the following steps: S1, the original image of an unmanned aerial vehicle polling transmission circuit is taken as an image source, positions of parts such as a tower, an insulator, a grading ring, a spacer and the like in the original image, attribute labels are added for each part, and a transmission circuit part identification training data set is constructed; S2, a convolutional neural network and a feature pyramid network is employed to extract the multi-layer features of a transmission circuit image; S3, the extracted image features and the calibrated label data are taken as training input data, a position sensitive scoring graph is calculated, the loss value of classification and regression networks is calculated, and the stochastic gradient descent is employed to optimize parameters of classification network and the regression network so as to realize optimal classification and location of the parts in the training data; and S4, according to the training parameters obtained according to the transmission network identification training, the transmission circuit polling data is introduced in batch to realize automatic location and classification of the parts.

The fruit quality real-time detection and sorting robot system is formed from fruit conveying and turning component, computer vision identification component and autoamtic sorting component. The double-cone rollers of fruit conveying and turning component can make fruits, automatically form single line arrangement, and can forward convey fruits at a certain speed, at the same time make the fruits uniformly turn around horizontal small shaft so as to make computer vision identification component detect whole surface of every fruit, including shape, size, colour, surface smoothness and surface defect and injury and obtain quality information of every fruit, and define its grade and position information, said information can be transferred into automatic sorting component to make fruit fall into its correspondent fruit collecting box so as to implement automatic of fruit detection and sorting process.

The invention discloses pearl real-time detection and grading system based on machine vision which includes: computer vision recognition component which is equipped under the charging conduit of pearl charging device, and pearl sorting device is equipped under the computer vision recognition component. Using the computer vision technology, collecting image of sequential free-falling pearl surface, sending the collected image to DSP and computers and other hardware apparatus to dealing, then distinguishing the pearl classification standard instantaneously through the dealing results, last sending the distinguishing information to sorting device to achieving classification of pearl with different characteristics automatically using devices. It can get pearl whole surface image, can complete the testing of whole apparent quality index such as pearl size, shape, smooth, defect and color, etc. simultaneously, achieve the rapid real-time detection and grading. The system can adapt to different classification speed, and the structure is simple, adaptability is strong.

The invention provides a method for assimilating remote sensing data of soil humidity in a watershed scale. The method comprises the following steps of: improving a watershed runoff producing calculation module and developing a distributed hydrological model which is suitable for assimilating remote sensing soil humidity information and describes a soil hydrodynamic process; introducing a particle filtering sequence data assimilation method of information science, and continuously merging and assimilating new remote sensing observation data in a dynamic operation process of distributed hydrological process numerical simulation so as to acquire updated watershed soil humidity assimilated data during sequential assimilation; feeding the updated watershed soil humidity assimilated data back to a distributed hydrological model platform; and gradually estimating the time and space distribution pattern of watershed soil moisture content. Practices prove that by the method, not only high-precision and physically consistent watershed soil humidity data can be provided for research on hydrology, zoology, environment and agriculture, but also the foundation is laid for performing four-dimensional data assimilation processing on soil humidity data of an upper soil layer acquired by using remote sensing retrieval, and improving the precision of the model.

The invention discloses a method and a system for detecting a polymorphism locus of a genome target region. The method comprises the steps of: obtaining an exon sequencing result, removing redundancy and sequencing, carrying out statistic analysis I, detecting an SNP (Single Nucleotide Polymorphism) locus, filtering the SNP locus, carrying out statistic analysis II and noting the SNP. The SNP analysis can be carried out by sequencing a genome specific region; and the invention has the advantages of high detection accuracy of SNP result, high speed and low cost, and can realize the automation in the whole process, i.e. the high-quality SNP locus is automatically generated by using original sequencing data as a data source, and the SNP locus can be noted and classified.

The invention discloses a seedling transplanting system based on machine vision and consists of components of seedling transportation, machine vision identification, control and transplanting. A conveyor belt of the seedling transportation component can convey seedling plates to the transplanting positions and automatically convey after the completion of the transplanting; color images obtained from the machine vision system are used for detecting a plurality of appearance growth indicators such as the size, the number of leaves of the seedlings so as to comprehensively judge whether the seedlings are suitable for transplanting and meanwhile determine the location information of the seedlings to be transferred to the control system by a computer through a serial communication interface RS-232; then the PLC of the control system sends commands to an end effector to realize automatic transplantation of the seedlings through the control system; the end effector adopts shovel-shaped fingers driven by a linear cylinder and meets the seedling transplanting demand for seedling plates with different sizes through regulating the angle of fingers. The invention utilizes the vision system to obtain the growth and location information of the seedlings and realizes the transplantation through the control of the computer and the PLC.

The invention discloses a night vehicle video detection method based on illumination visibility identification, which comprises the following steps of: 1) collecting a night traffic scene video image: using a camera lens to compress the collected traffic scene video image into MPEG (moving picture experts group) format and further transmitting to a computer for storage; 2) identifying the night illumination mode: determining whether the mode is the night mode with street lamps or the night mode without the street lamps; 3) carrying out vehicle detection under the night mode without the street lamps or carrying out the vehicle detection under the night mode with the street lamps; 4) tracking the motion of a night vehicle: utilizing the kalman filtering algorithm to carry out motion tracking on a matched vehicle headlamp (under the night mode without the street lamps) or the whole vehicle (under the night mode with the street lamps), obtaining the motion state of the vehicle and realizing the continuous and fast tracking of the motion of the vehicle; and 5) extracting a traffic parameter of the night vehicle: and adopting the two-dimensional reconstruction algorithm based on black box calibration to realize the extraction of a running speed parameter of the vehicle according to a projection relationship model between an image coordinate and a world coordinate.

A user has been unable to efficiently acquire desired information from a website even if it is a website that the user has browsed at least once before. There is provided an information processing device, which comprises: a browse history analysis portion which extracts information conforming with a predetermined subject from received data of a first device, and corresponding said information to reference data of said received data to store the corresponded data in a storage portion; and an information retrieval portion which receives an information retrieval request sent from a second device, along with a designation of a field subclassed in said predetermined subject, retrieves said storage portion from the information conforming with said predetermined subject, extracts said reference data corresponding to said information, and transmits same to said second device.

The invention discloses a network security situation awareness system based on self-discipline computing and a processing method thereof. The network security situation awareness system comprises a managed resource, an Agent cooperation layer module, a sensor, an effector module and a self-discipline manager module, wherein the Agent cooperation layer module is connected with the managed resource and the self-discipline manager module; and the sensor and the effector module are respectively connected with the Agent cooperation layer module and the self-discipline manager module. The network security situation awareness system has the advantages that the system structure and the situation extraction for situation awareness are improved, the system environment is adjusted autonomously to adapt to the environment change dynamically, so that dynamic allocation of resources, dynamic synthesis of services and dynamic correction of system parameters are realized.

A compressed sensing signal collection method based on filtering includes the following steps: firstly, sensing equipment is used for collecting target signals x (t) in an independent sampling period and carries out digital quantification on the signals in an analog/digit (A/D) mode. Secondly, the dimension of the quantified signals x (i) is reduced. Lastly, the signals with the reduced dimension are reconfigured. The t means the sampling time, and the i means the sequence of the quantified signals. The detailed method of dimension-reduction of the quantified signals is that the quantified signals respond to a difference equation of a filter through finite impulse, and the difference equation is that i= 1, ..., M, wherein h (0), ..., h (L-1) is the coefficients of the filter. The design constructs a following toeplitz measurement array based on a compressed sensing signal collection framework of the filter, and the toeplitz measurement array is that i= 1, ..., M is observed, wherein b1,..., bL are treated as coefficients of the filter. The singular value of a sub-array phi FT is an arithmetic value of a characteristic value of a Gramm array which is that G (phi F, T) = phi ` FT phi FT, all the characteristic values that lambada i belongs to (1- delta K, 1+ delta K), wherein i= 1, ..., T of G (phi F, T) are tested, and the original signals are reconfigured by solving the following l1 optimization problems.

The invention discloses a method for sending an uplink sounding reference signal in a multi-carrier system, and the method is applied in the multi-carrier system and comprises the following steps: determining the time position of sending the uplink sounding reference signal (SRS) on a working carrier; determining the time position of sending the SRS signal on a non-working carrier based on the principle of time division multiple access (TDM) in the multi-carrier; and sending the SRS signal in the determined time position of the working carrier and the time position of the non-working carrier,wherein each time position comprises a sign position or a sub-frame and the like. The invention further discloses a user equipment which comprises a working carrier time position determining unit, a non-working carrier time position determining unit and an SRS signal sending unit. The scheme of the invention can achieve the purpose of sending the SRS signal at a certain bandwidth stipulated by all carriers of the multi-carrier system or the communication protocol within a certain time.

The invention relates to a road network grid matching method, which comprises: obtaining GPS original point data and de-noising the GPS original point data to gain GPS point data; partitioning the road network into grids according to the GIS road network structure information to get road network grid units; and matching the GPS point data to the corresponding road network grid units to obtain grid matching data. The invention also relates to an information acquisition method for real-time road conditions, which comprises: matching road network grids to get grid matching data; integrating the grid matching data with GPS traffic flow data; and filtering the integrated data according to types of driving routes to obtain the information of real-time road conditions. The invention provides theroad network grid matching method based on the GPS road network grid partitioning technique aiming at large concurrent GPS data for achieving real-time treatment of the large concurrent GPS data; andacquires information of real-time road conditions and real-time guiding routes by using the road network grid matching method.

The invention relates to a method of quickly segmenting a moving target in a non-restrictive scene based on a full convolution network, which belongs to the technical field of video object segmentation. The method comprises steps: firstly, framing is carried out on the video, and a result after framing is used for making a Ground Truth set S for a sample image; a full convolution neural network trained through a PASCAL VOC standard library is adopted to predict a target in each frame of the video, a deep feature estimator for an image foreground target is acquired, target maximum intra-class likelihood mapping information in all frames is obtained hereby, and initial prediction on the foreground and the background in the video frames is realized; and then, through a Markov random field, deep feature estimators for the foreground and the background are refined, and thus, segmentation on the video foreground moving target in the non-restrictive scene video can be realized. The information of the moving target can be effectively acquired, high-efficiency and accurate segmentation on the moving target can be realized, and the analysis precision of the video foreground-background information is improved.

The invention relates to a multi-level-point-set characteristic extraction method applicable to ground laser radar point cloud classification. Based on point set characteristics, high-precision classification of four kinds of common ground features including pedestrians, trees, buildings and automobiles and the like in a scene is realized. Firstly, point sets are constructed and a point cloud is re-sampled into a point cloud of different scales and thus point sets which are different in size and provided with layered structures are formed through clustering and characteristics of each point in the point sets are obtained; next, an LDA (Latent Dirichlet Allocation ) method is adopted to synthesizing point-based characteristics of all points in each point set into shape characteristics of the point sets; and at last, based on the shape characteristics of the point set, an Adaboost classifier is adopted to train the point sets of different levels so as to obtain a classification result of the whole point cloud. The multi-level-point-set characteristic extraction method has a higher classification precision and has a classification precision, which is far higher than that of point-based characteristics, Bag-of-Word-based characteristics and characteristics based on probabilistic latent semantic analysis (PLSA), in aspect of pedestrians and vehicles.